AI Boosts Research Careers but Flattens Scientific Discovery

> “It’s not about the architecture per se,” Evans says. “It’s about the incentives.”

It would have been useful to check whether less original work was already getting more citations before AI adoption. That could reflect broader trends and network effects: heavily cited research areas attract more authors optimizing for citations, so high-productivity researchers end up clustering on the same topics.

As with other fields touched, AI is merely amplifying what was already there. The aim of many scientists isn't discovery in and of itself. Discovery is a side effect of their primary drive to publish and - hopefully - become well known. And establishments only make things worse, because it's the things that are most likely to produce tangible results (the papers, or economically valuable products) that get the most funding.

Any flattening of discovery due to AI, but will be temporary.

We tend to think that obvious potential is the same as realized potential, for new technology.

For any specific context, there are generally innumerable smaller adaptations and capability thresholds that have to be crossed. And the price for that journey is often temporary loss off overt productivity.

This isn't a real surprise to anyone who knows how "AI" works.

“Technology that is based on everything humanity has already done, fails to do things that humanity has not yet done”